HOUSE PRICING PROJECT¶

Import libraries¶

In [1]:
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
from matplotlib.pyplot import show

Reading Traning Dataset¶

In [2]:
df1=pd.read_csv('D:/training_set.csv')
df1
Out[2]:
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition SalePrice
0 1 60 RL 65.0 8450 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 2 2008 WD Normal 208500
1 2 20 RL 80.0 9600 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 5 2007 WD Normal 181500
2 3 60 RL 68.0 11250 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 9 2008 WD Normal 223500
3 4 70 RL 60.0 9550 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 2 2006 WD Abnorml 140000
4 5 60 RL 84.0 14260 Pave NaN IR1 Lvl AllPub ... 0 NaN NaN NaN 0 12 2008 WD Normal 250000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1455 1456 60 RL 62.0 7917 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 8 2007 WD Normal 175000
1456 1457 20 RL 85.0 13175 Pave NaN Reg Lvl AllPub ... 0 NaN MnPrv NaN 0 2 2010 WD Normal 210000
1457 1458 70 RL 66.0 9042 Pave NaN Reg Lvl AllPub ... 0 NaN GdPrv Shed 2500 5 2010 WD Normal 266500
1458 1459 20 RL 68.0 9717 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 4 2010 WD Normal 142125
1459 1460 20 RL 75.0 9937 Pave NaN Reg Lvl AllPub ... 0 NaN NaN NaN 0 6 2008 WD Normal 147500

1460 rows × 81 columns

In [3]:
df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   int64  
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          91 non-null     object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     1452 non-null   object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1423 non-null   object 
 31  BsmtCond       1423 non-null   object 
 32  BsmtExposure   1422 non-null   object 
 33  BsmtFinType1   1423 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1422 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    770 non-null    object 
 58  GarageType     1379 non-null   object 
 59  GarageYrBlt    1379 non-null   float64
 60  GarageFinish   1379 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1379 non-null   object 
 64  GarageCond     1379 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         7 non-null      object 
 73  Fence          281 non-null    object 
 74  MiscFeature    54 non-null     object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB
In [4]:
df1['SaleCondition'].value_counts()
Out[4]:
Normal     1198
Partial     125
Abnorml     101
Family       20
Alloca       12
AdjLand       4
Name: SaleCondition, dtype: int64
In [5]:
df1.shape
Out[5]:
(1460, 81)

Missing Data Treatment (Filling Null values)¶

In [6]:
df1.isna().sum()
Out[6]:
Id                 0
MSSubClass         0
MSZoning           0
LotFrontage      259
LotArea            0
                ... 
MoSold             0
YrSold             0
SaleType           0
SaleCondition      0
SalePrice          0
Length: 81, dtype: int64
In [7]:
for i in df1.columns:
        if (df1[i].isna().sum())>0:
            if df1[i].dtypes=='object':
                x=df1[i].mode()[0]
                df1[i]=df1[i].fillna(x)
            else:
                x=df1[i].mean()
                df1[i]=df1[i].fillna(x)
In [8]:
df1.isna().sum()
Out[8]:
Id               0
MSSubClass       0
MSZoning         0
LotFrontage      0
LotArea          0
                ..
MoSold           0
YrSold           0
SaleType         0
SaleCondition    0
SalePrice        0
Length: 81, dtype: int64

Droping irrelevant columns¶

Seperating Input & Output features (X & Y)¶

In [9]:
X=df1.drop(labels=['SalePrice','Id','LowQualFinSF','MiscVal'],axis=1)
Y=df1['SalePrice']
In [10]:
X.shape
Out[10]:
(1460, 77)
In [11]:
Y.shape
Out[11]:
(1460,)

Seperating Categorical & Continuous columns¶

In [12]:
cat=[]
con=[]
for i in X.columns:
    if X[i].dtypes=='object':
        cat.append(i)
    else:
        con.append(i)
print(cat)
print(con)

['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']
['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MoSold', 'YrSold']
In [13]:
cat=['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 
     'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 
     'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 
     'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 
     'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']
con=['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 
     'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 
     'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 
     'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 
     'MoSold', 'YrSold']

Analysis¶

Univariant Analysis¶

In [14]:
for i in df1.columns:
    if df1[i].dtypes=='object':
        df1[i].value_counts().plot(kind='bar')
        plt.show()
    else:
        sb.histplot(data=df1,x=df1[i],kde=True)
        plt.show()

Bivariant Analysis¶

In [15]:
for i in df1.columns:
    if df1[i].dtypes=='object':
        sb.boxplot(data=df1,x=df1[i],y='SalePrice')
        plt.show()
    else:
        plt.scatter(data=df1,x=df1[i],y='SalePrice')
        plt.xlabel(i)
        plt.ylabel('SalePrice')
        plt.title(f'{i} vs SalePrice')
        plt.show()

Multivarient Analysis¶

In [16]:
a=df1.corr()
a

C:\Users\Alankar\AppData\Local\Temp\ipykernel_13472\2980587853.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  a=df1.corr()
Out[16]:
Id MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold SalePrice
Id 1.000000 0.011156 -0.009601 -0.033226 -0.028365 0.012609 -0.012713 -0.021998 -0.050199 -0.005024 ... -0.029643 -0.000477 0.002889 -0.046635 0.001330 0.057044 -0.006242 0.021172 0.000712 -0.021917
MSSubClass 0.011156 1.000000 -0.357056 -0.139781 0.032628 -0.059316 0.027850 0.040581 0.022895 -0.069836 ... -0.012579 -0.006100 -0.012037 -0.043825 -0.026030 0.008283 -0.007683 -0.013585 -0.021407 -0.084284
LotFrontage -0.009601 -0.357056 1.000000 0.306795 0.234196 -0.052820 0.117598 0.082746 0.179283 0.215828 ... 0.077106 0.137454 0.009790 0.062335 0.037684 0.180868 0.001168 0.010158 0.006768 0.334901
LotArea -0.033226 -0.139781 0.306795 1.000000 0.105806 -0.005636 0.014228 0.013788 0.103960 0.214103 ... 0.171698 0.084774 -0.018340 0.020423 0.043160 0.077672 0.038068 0.001205 -0.014261 0.263843
OverallQual -0.028365 0.032628 0.234196 0.105806 1.000000 -0.091932 0.572323 0.550684 0.410238 0.239666 ... 0.238923 0.308819 -0.113937 0.030371 0.064886 0.065166 -0.031406 0.070815 -0.027347 0.790982
OverallCond 0.012609 -0.059316 -0.052820 -0.005636 -0.091932 1.000000 -0.375983 0.073741 -0.127788 -0.046231 ... -0.003334 -0.032589 0.070356 0.025504 0.054811 -0.001985 0.068777 -0.003511 0.043950 -0.077856
YearBuilt -0.012713 0.027850 0.117598 0.014228 0.572323 -0.375983 1.000000 0.592855 0.314745 0.249503 ... 0.224880 0.188686 -0.387268 0.031355 -0.050364 0.004950 -0.034383 0.012398 -0.013618 0.522897
YearRemodAdd -0.021998 0.040581 0.082746 0.013788 0.550684 0.073741 0.592855 1.000000 0.179186 0.128451 ... 0.205726 0.226298 -0.193919 0.045286 -0.038740 0.005829 -0.010286 0.021490 0.035743 0.507101
MasVnrArea -0.050199 0.022895 0.179283 0.103960 0.410238 -0.127788 0.314745 0.179186 1.000000 0.263582 ... 0.159349 0.124965 -0.109849 0.018795 0.061453 0.011723 -0.029815 -0.005940 -0.008184 0.475241
BsmtFinSF1 -0.005024 -0.069836 0.215828 0.214103 0.239666 -0.046231 0.249503 0.128451 0.263582 1.000000 ... 0.204306 0.111761 -0.102303 0.026451 0.062021 0.140491 0.003571 -0.015727 0.014359 0.386420
BsmtFinSF2 -0.005968 -0.065649 0.043340 0.111170 -0.059119 0.040229 -0.049107 -0.067759 -0.072302 -0.050117 ... 0.067898 0.003093 0.036543 -0.029993 0.088871 0.041709 0.004940 -0.015211 0.031706 -0.011378
BsmtUnfSF -0.007940 -0.140759 0.122156 -0.002618 0.308159 -0.136841 0.149040 0.181133 0.114184 -0.495251 ... -0.005316 0.129005 -0.002538 0.020764 -0.012579 -0.035092 -0.023837 0.034888 -0.041258 0.214479
TotalBsmtSF -0.015415 -0.238518 0.363358 0.260833 0.537808 -0.171098 0.391452 0.291066 0.362452 0.522396 ... 0.232019 0.247264 -0.095478 0.037384 0.084489 0.126053 -0.018479 0.013196 -0.014969 0.613581
1stFlrSF 0.010496 -0.251758 0.414266 0.299475 0.476224 -0.144203 0.281986 0.240379 0.342160 0.445863 ... 0.235459 0.211671 -0.065292 0.056104 0.088758 0.131525 -0.021096 0.031372 -0.013604 0.605852
2ndFlrSF 0.005590 0.307886 0.072483 0.050986 0.295493 0.028942 0.010308 0.140024 0.174019 -0.137079 ... 0.092165 0.208026 0.061989 -0.024358 0.040606 0.081487 0.016197 0.035164 -0.028700 0.319334
LowQualFinSF -0.044230 0.046474 0.036849 0.004779 -0.030429 0.025494 -0.183784 -0.062419 -0.069068 -0.064503 ... -0.025444 0.018251 0.061081 -0.004296 0.026799 0.062157 -0.003793 -0.022174 -0.028921 -0.025606
GrLivArea 0.008273 0.074853 0.368392 0.263116 0.593007 -0.079686 0.199010 0.287389 0.389893 0.208171 ... 0.247433 0.330224 0.009113 0.020643 0.101510 0.170205 -0.002416 0.050240 -0.036526 0.708624
BsmtFullBath 0.002289 0.003491 0.091481 0.158155 0.111098 -0.054942 0.187599 0.119470 0.085055 0.649212 ... 0.175315 0.067341 -0.049911 -0.000106 0.023148 0.067616 -0.023047 -0.025361 0.067049 0.227122
BsmtHalfBath -0.020155 -0.002333 -0.006419 0.048046 -0.040150 0.117821 -0.038162 -0.012337 0.026669 0.067418 ... 0.040161 -0.025324 -0.008555 0.035114 0.032121 0.020025 -0.007367 0.032873 -0.046524 -0.016844
FullBath 0.005587 0.131608 0.180424 0.126031 0.550600 -0.194149 0.468271 0.439046 0.275730 0.058543 ... 0.187703 0.259977 -0.115093 0.035353 -0.008106 0.049604 -0.014290 0.055872 -0.019669 0.560664
HalfBath 0.006784 0.177354 0.048258 0.014259 0.273458 -0.060769 0.242656 0.183331 0.200802 0.004262 ... 0.108080 0.199740 -0.095317 -0.004972 0.072426 0.022381 0.001290 -0.009050 -0.010269 0.284108
BedroomAbvGr 0.037719 -0.023438 0.237023 0.119690 0.101676 0.012980 -0.070651 -0.040581 0.102417 -0.107355 ... 0.046854 0.093810 0.041570 -0.024478 0.044300 0.070703 0.007767 0.046544 -0.036014 0.168213
KitchenAbvGr 0.002951 0.281721 -0.005805 -0.017784 -0.183882 -0.087001 -0.174800 -0.149598 -0.037364 -0.081007 ... -0.090130 -0.070091 0.037312 -0.024600 -0.051613 -0.014525 0.062341 0.026589 0.031687 -0.135907
TotRmsAbvGrd 0.027239 0.040380 0.320146 0.190015 0.427452 -0.057583 0.095589 0.191740 0.280027 0.044316 ... 0.165984 0.234192 0.004151 -0.006683 0.059383 0.083757 0.024763 0.036907 -0.034516 0.533723
Fireplaces -0.019772 -0.045569 0.235755 0.271364 0.396765 -0.023820 0.147716 0.112581 0.247906 0.260011 ... 0.200019 0.169405 -0.024822 0.011257 0.184530 0.095074 0.001409 0.046357 -0.024096 0.466929
GarageYrBlt 0.000070 0.080187 0.064324 -0.024812 0.518018 -0.306169 0.780555 0.618130 0.249367 0.150338 ... 0.220623 0.218490 -0.285882 0.023534 -0.075256 -0.014499 -0.031853 0.005173 -0.000987 0.470177
GarageCars 0.016570 -0.040110 0.269729 0.154871 0.600671 -0.185758 0.537850 0.420622 0.363778 0.224054 ... 0.226342 0.213569 -0.151434 0.035765 0.050494 0.020934 -0.043080 0.040522 -0.039117 0.640409
GarageArea 0.017634 -0.098672 0.323663 0.180403 0.562022 -0.151521 0.478954 0.371600 0.372567 0.296970 ... 0.224666 0.241435 -0.121777 0.035087 0.051412 0.061047 -0.027400 0.027974 -0.027378 0.623431
WoodDeckSF -0.029643 -0.012579 0.077106 0.171698 0.238923 -0.003334 0.224880 0.205726 0.159349 0.204306 ... 1.000000 0.058661 -0.125989 -0.032771 -0.074181 0.073378 -0.009551 0.021011 0.022270 0.324413
OpenPorchSF -0.000477 -0.006100 0.137454 0.084774 0.308819 -0.032589 0.188686 0.226298 0.124965 0.111761 ... 0.058661 1.000000 -0.093079 -0.005842 0.074304 0.060762 -0.018584 0.071255 -0.057619 0.315856
EnclosedPorch 0.002889 -0.012037 0.009790 -0.018340 -0.113937 0.070356 -0.387268 -0.193919 -0.109849 -0.102303 ... -0.125989 -0.093079 1.000000 -0.037305 -0.082864 0.054203 0.018361 -0.028887 -0.009916 -0.128578
3SsnPorch -0.046635 -0.043825 0.062335 0.020423 0.030371 0.025504 0.031355 0.045286 0.018795 0.026451 ... -0.032771 -0.005842 -0.037305 1.000000 -0.031436 -0.007992 0.000354 0.029474 0.018645 0.044584
ScreenPorch 0.001330 -0.026030 0.037684 0.043160 0.064886 0.054811 -0.050364 -0.038740 0.061453 0.062021 ... -0.074181 0.074304 -0.082864 -0.031436 1.000000 0.051307 0.031946 0.023217 0.010694 0.111447
PoolArea 0.057044 0.008283 0.180868 0.077672 0.065166 -0.001985 0.004950 0.005829 0.011723 0.140491 ... 0.073378 0.060762 0.054203 -0.007992 0.051307 1.000000 0.029669 -0.033737 -0.059689 0.092404
MiscVal -0.006242 -0.007683 0.001168 0.038068 -0.031406 0.068777 -0.034383 -0.010286 -0.029815 0.003571 ... -0.009551 -0.018584 0.018361 0.000354 0.031946 0.029669 1.000000 -0.006495 0.004906 -0.021190
MoSold 0.021172 -0.013585 0.010158 0.001205 0.070815 -0.003511 0.012398 0.021490 -0.005940 -0.015727 ... 0.021011 0.071255 -0.028887 0.029474 0.023217 -0.033737 -0.006495 1.000000 -0.145721 0.046432
YrSold 0.000712 -0.021407 0.006768 -0.014261 -0.027347 0.043950 -0.013618 0.035743 -0.008184 0.014359 ... 0.022270 -0.057619 -0.009916 0.018645 0.010694 -0.059689 0.004906 -0.145721 1.000000 -0.028923
SalePrice -0.021917 -0.084284 0.334901 0.263843 0.790982 -0.077856 0.522897 0.507101 0.475241 0.386420 ... 0.324413 0.315856 -0.128578 0.044584 0.111447 0.092404 -0.021190 0.046432 -0.028923 1.000000

38 rows × 38 columns

In [17]:
sb.heatmap(a)
Out[17]:
<Axes: >

Standardisation¶

In [18]:
from sklearn.preprocessing import StandardScaler
ss=StandardScaler()
X1=pd.DataFrame(ss.fit_transform(X[con]),columns=con)
X1
Out[18]:
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MoSold YrSold
0 0.073375 -0.229372 -0.207142 0.651479 -0.517200 1.050994 0.878668 0.511418 0.575425 -0.288653 ... 0.311725 0.351000 -0.752176 0.216503 -0.359325 -0.116339 -0.270208 -0.068692 -1.599111 0.138777
1 -0.872563 0.451936 -0.091886 -0.071836 2.179628 0.156734 -0.429577 -0.574410 1.171992 -0.288653 ... 0.311725 -0.060731 1.626195 -0.704483 -0.359325 -0.116339 -0.270208 -0.068692 -0.489110 -0.614439
2 0.073375 -0.093110 0.073480 0.651479 -0.517200 0.984752 0.830215 0.323060 0.092907 -0.288653 ... 0.311725 0.631726 -0.752176 -0.070361 -0.359325 -0.116339 -0.270208 -0.068692 0.990891 0.138777
3 0.309859 -0.456474 -0.096897 0.651479 -0.517200 -1.863632 -0.720298 -0.574410 -0.499274 -0.288653 ... 1.650307 0.790804 -0.752176 -0.176048 4.092524 -0.116339 -0.270208 -0.068692 -1.599111 -1.367655
4 0.073375 0.633618 0.375148 1.374795 -0.517200 0.951632 0.733308 1.364570 0.463568 -0.288653 ... 1.650307 1.698485 0.780197 0.563760 -0.359325 -0.116339 -0.270208 -0.068692 2.100892 0.138777
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1455 0.073375 -0.365633 -0.260560 -0.071836 -0.517200 0.918511 0.733308 -0.574410 -0.973018 -0.288653 ... 0.311725 -0.060731 -0.752176 -0.100558 -0.359325 -0.116339 -0.270208 -0.068692 0.620891 -0.614439
1456 -0.872563 0.679039 0.266407 -0.071836 0.381743 0.222975 0.151865 0.084843 0.759659 0.722112 ... 0.311725 0.126420 2.033231 -0.704483 -0.359325 -0.116339 -0.270208 -0.068692 -1.599111 1.645210
1457 0.309859 -0.183951 -0.147810 0.651479 3.078570 -1.002492 1.024029 -0.574410 -0.369871 -0.288653 ... -1.026858 -1.033914 -0.752176 0.201405 -0.359325 -0.116339 -0.270208 -0.068692 -0.489110 1.645210
1458 -0.872563 -0.093110 -0.080160 -0.795151 0.381743 -0.704406 0.539493 -0.574410 -0.865548 6.092188 ... -1.026858 -1.090059 2.168910 -0.704483 1.473789 -0.116339 -0.270208 -0.068692 -0.859110 1.645210
1459 -0.872563 0.224833 -0.058112 -0.795151 0.381743 -0.207594 -0.962566 -0.574410 0.847389 1.509640 ... -1.026858 -0.921624 5.121921 0.322190 -0.359325 -0.116339 -0.270208 -0.068692 -0.119110 0.138777

1460 rows × 34 columns

Encoding ( converting Categorical cloumns in numerical)¶

OneHot Encoding¶

In [19]:
X2=pd.get_dummies(X[cat])
X2
Out[19]:
MSZoning_C (all) MSZoning_FV MSZoning_RH MSZoning_RL MSZoning_RM Street_Grvl Street_Pave Alley_Grvl Alley_Pave LotShape_IR1 ... SaleType_ConLw SaleType_New SaleType_Oth SaleType_WD SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial
0 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
2 0 0 0 1 0 0 1 1 0 1 ... 0 0 0 1 0 0 0 0 1 0
3 0 0 0 1 0 0 1 1 0 1 ... 0 0 0 1 1 0 0 0 0 0
4 0 0 0 1 0 0 1 1 0 1 ... 0 0 0 1 0 0 0 0 1 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1455 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1456 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1457 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1458 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1459 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0

1460 rows × 252 columns

Detecting & Treating Outliers¶

In [20]:
out=[]
for i in con:
    out.extend((X1[(X1[i]>3) | (X1[i]<-3)]).index)
print(out)

[9, 48, 93, 125, 165, 246, 291, 300, 312, 335, 411, 488, 520, 535, 635, 637, 703, 705, 713, 861, 969, 985, 1030, 1062, 1144, 1186, 1190, 1266, 1393, 1416, 171, 197, 231, 277, 313, 446, 807, 909, 934, 1107, 1127, 1173, 1182, 1211, 1298, 1337, 53, 249, 313, 335, 384, 451, 457, 661, 706, 769, 848, 1298, 1396, 375, 533, 88, 185, 191, 218, 241, 250, 304, 375, 378, 398, 461, 508, 519, 583, 676, 703, 726, 745, 980, 991, 1123, 1149, 1213, 1268, 1327, 1352, 1435, 1457, 304, 630, 747, 1132, 1137, 1349, 37, 58, 70, 105, 115, 161, 169, 178, 224, 297, 349, 403, 477, 517, 523, 654, 691, 718, 755, 763, 798, 808, 825, 898, 981, 1111, 1169, 1228, 1289, 1298, 1373, 1417, 70, 178, 523, 898, 1182, 1298, 24, 52, 113, 116, 153, 166, 233, 253, 260, 263, 271, 273, 313, 322, 355, 414, 440, 446, 470, 493, 542, 548, 577, 586, 599, 666, 697, 764, 785, 828, 842, 854, 888, 918, 923, 924, 1040, 1059, 1077, 1152, 1220, 1253, 1299, 1308, 1320, 1369, 1387, 1418, 1445, 1458, 137, 224, 278, 477, 496, 581, 678, 774, 798, 932, 1267, 224, 332, 440, 496, 523, 691, 1044, 1182, 1298, 1373, 224, 440, 496, 523, 529, 691, 898, 1024, 1044, 1182, 1298, 1373, 304, 691, 1169, 1182, 118, 185, 197, 304, 496, 523, 608, 635, 691, 769, 798, 1169, 1182, 1268, 1298, 1353, 53, 188, 313, 326, 335, 420, 454, 588, 634, 738, 807, 921, 942, 1163, 1270, 1298, 1, 26, 33, 37, 41, 50, 93, 116, 129, 176, 197, 201, 213, 215, 218, 245, 249, 251, 253, 298, 299, 314, 330, 352, 358, 367, 414, 421, 426, 499, 504, 558, 574, 576, 580, 597, 611, 628, 633, 658, 691, 697, 717, 741, 743, 745, 814, 828, 892, 920, 925, 931, 944, 952, 953, 954, 1006, 1029, 1041, 1047, 1052, 1055, 1069, 1072, 1076, 1080, 1103, 1118, 1123, 1149, 1156, 1181, 1213, 1225, 1276, 1287, 1327, 1335, 1350, 1389, 1405, 1415, 188, 298, 597, 624, 628, 921, 1154, 1163, 1230, 1283, 1350, 1450, 53, 144, 189, 291, 330, 570, 634, 635, 843, 897, 1163, 1213, 1270, 1350, 8, 9, 17, 39, 48, 74, 78, 93, 102, 137, 144, 165, 188, 246, 330, 342, 420, 441, 454, 488, 505, 520, 529, 570, 634, 635, 637, 676, 703, 705, 728, 736, 778, 809, 843, 886, 894, 897, 910, 913, 921, 940, 942, 943, 954, 955, 984, 1003, 1011, 1030, 1062, 1090, 1163, 1186, 1216, 1230, 1232, 1266, 1275, 1283, 1292, 1336, 1350, 1391, 1393, 1412, 1416, 1450, 185, 635, 769, 803, 897, 910, 1031, 1173, 1230, 1298, 1350, 1386, 166, 309, 605, 642, 1298, 93, 653, 178, 581, 664, 825, 1061, 1190, 1298, 53, 64, 166, 169, 335, 343, 357, 480, 661, 769, 828, 848, 893, 961, 974, 1044, 1068, 1210, 1312, 1313, 1423, 1459, 28, 185, 293, 495, 499, 523, 583, 591, 645, 664, 666, 713, 735, 775, 784, 807, 854, 875, 947, 961, 996, 1184, 1193, 1292, 1298, 1328, 1369, 3, 7, 154, 197, 260, 306, 314, 325, 328, 358, 365, 380, 459, 462, 496, 520, 577, 630, 648, 653, 660, 662, 718, 720, 747, 799, 813, 836, 840, 918, 939, 945, 1013, 1030, 1081, 1119, 1139, 1150, 1152, 1185, 1197, 1202, 1248, 1266, 1326, 1360, 1382, 1393, 1419, 1439, 1445, 5, 55, 120, 129, 159, 182, 187, 205, 237, 258, 280, 546, 704, 726, 744, 889, 941, 1080, 1156, 1161, 1181, 1346, 1437, 46, 72, 80, 104, 176, 185, 189, 196, 289, 297, 312, 339, 351, 359, 360, 366, 400, 426, 471, 475, 550, 605, 618, 625, 647, 673, 764, 769, 785, 795, 803, 828, 830, 854, 859, 887, 888, 907, 919, 944, 1037, 1055, 1067, 1070, 1154, 1171, 1184, 1228, 1282, 1293, 1301, 1320, 1328, 1386, 1414, 197, 810, 1170, 1182, 1298, 1386, 1423]
In [21]:
import numpy as np
outliers=np.unique(out)
outliers
Out[21]:
array([   1,    3,    5,    7,    8,    9,   17,   24,   26,   28,   33,
         37,   39,   41,   46,   48,   50,   52,   53,   55,   58,   64,
         70,   72,   74,   78,   80,   88,   93,  102,  104,  105,  113,
        115,  116,  118,  120,  125,  129,  137,  144,  153,  154,  159,
        161,  165,  166,  169,  171,  176,  178,  182,  185,  187,  188,
        189,  191,  196,  197,  201,  205,  213,  215,  218,  224,  231,
        233,  237,  241,  245,  246,  249,  250,  251,  253,  258,  260,
        263,  271,  273,  277,  278,  280,  289,  291,  293,  297,  298,
        299,  300,  304,  306,  309,  312,  313,  314,  322,  325,  326,
        328,  330,  332,  335,  339,  342,  343,  349,  351,  352,  355,
        357,  358,  359,  360,  365,  366,  367,  375,  378,  380,  384,
        398,  400,  403,  411,  414,  420,  421,  426,  440,  441,  446,
        451,  454,  457,  459,  461,  462,  470,  471,  475,  477,  480,
        488,  493,  495,  496,  499,  504,  505,  508,  517,  519,  520,
        523,  529,  533,  535,  542,  546,  548,  550,  558,  570,  574,
        576,  577,  580,  581,  583,  586,  588,  591,  597,  599,  605,
        608,  611,  618,  624,  625,  628,  630,  633,  634,  635,  637,
        642,  645,  647,  648,  653,  654,  658,  660,  661,  662,  664,
        666,  673,  676,  678,  691,  697,  703,  704,  705,  706,  713,
        717,  718,  720,  726,  728,  735,  736,  738,  741,  743,  744,
        745,  747,  755,  763,  764,  769,  774,  775,  778,  784,  785,
        795,  798,  799,  803,  807,  808,  809,  810,  813,  814,  825,
        828,  830,  836,  840,  842,  843,  848,  854,  859,  861,  875,
        886,  887,  888,  889,  892,  893,  894,  897,  898,  907,  909,
        910,  913,  918,  919,  920,  921,  923,  924,  925,  931,  932,
        934,  939,  940,  941,  942,  943,  944,  945,  947,  952,  953,
        954,  955,  961,  969,  974,  980,  981,  984,  985,  991,  996,
       1003, 1006, 1011, 1013, 1024, 1029, 1030, 1031, 1037, 1040, 1041,
       1044, 1047, 1052, 1055, 1059, 1061, 1062, 1067, 1068, 1069, 1070,
       1072, 1076, 1077, 1080, 1081, 1090, 1103, 1107, 1111, 1118, 1119,
       1123, 1127, 1132, 1137, 1139, 1144, 1149, 1150, 1152, 1154, 1156,
       1161, 1163, 1169, 1170, 1171, 1173, 1181, 1182, 1184, 1185, 1186,
       1190, 1193, 1197, 1202, 1210, 1211, 1213, 1216, 1220, 1225, 1228,
       1230, 1232, 1248, 1253, 1266, 1267, 1268, 1270, 1275, 1276, 1282,
       1283, 1287, 1289, 1292, 1293, 1298, 1299, 1301, 1308, 1312, 1313,
       1320, 1326, 1327, 1328, 1335, 1336, 1337, 1346, 1349, 1350, 1352,
       1353, 1360, 1369, 1373, 1382, 1386, 1387, 1389, 1391, 1393, 1396,
       1405, 1412, 1414, 1415, 1416, 1417, 1418, 1419, 1423, 1435, 1437,
       1439, 1445, 1450, 1457, 1458, 1459])

Droping Outliers from X & Y¶

In [22]:
X1.drop(index=outliers,axis=0,inplace=True)
X2.drop(index=outliers,axis=0,inplace=True)
Y.drop(index=outliers,axis=0,inplace=True)
In [23]:
X1.shape
Out[23]:
(1036, 34)
In [24]:
X2.shape
Out[24]:
(1036, 252)
In [25]:
Y.shape
Out[25]:
(1036,)

Joining X[Cat] & X[Con]¶

In [26]:
Xnew=X1.join(X2)
In [27]:
Xnew.shape
Out[27]:
(1036, 286)

Reindexing X & Y¶

In [28]:
Xnew.index=range(0,1036)
Y.index=range(0,1036)
In [29]:
Xnew.head()
Out[29]:
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... SaleType_ConLw SaleType_New SaleType_Oth SaleType_WD SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial
0 0.073375 -0.229372 -0.207142 0.651479 -0.5172 1.050994 0.878668 0.511418 0.575425 -0.288653 ... 0 0 0 1 0 0 0 0 1 0
1 0.073375 -0.093110 0.073480 0.651479 -0.5172 0.984752 0.830215 0.323060 0.092907 -0.288653 ... 0 0 0 1 0 0 0 0 1 0
2 0.073375 0.633618 0.375148 1.374795 -0.5172 0.951632 0.733308 1.364570 0.463568 -0.288653 ... 0 0 0 1 0 0 0 0 1 0
3 -0.872563 0.224833 -0.043379 1.374795 -0.5172 1.084115 0.975575 0.456019 2.029558 -0.288653 ... 0 0 0 1 0 0 0 0 1 0
4 -0.872563 -0.002269 0.068469 -0.795151 -0.5172 -0.207594 -0.962566 -0.574410 1.014077 -0.288653 ... 0 0 0 1 0 0 0 0 1 0

5 rows × 286 columns

In [30]:
Xnew.columns
Out[30]:
Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
       ...
       'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
       'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
       'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
       'SaleCondition_Partial'],
      dtype='object', length=286)

Spliting training & testing set¶

In [31]:
from sklearn.model_selection import train_test_split
In [32]:
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=45)
In [33]:
xtrain.shape
Out[33]:
(828, 286)
In [34]:
xtrain.columns
Out[34]:
Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
       ...
       'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
       'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
       'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
       'SaleCondition_Partial'],
      dtype='object', length=286)
In [35]:
ytrain.shape
Out[35]:
(828,)

Feature Selection¶

Backward Elimination¶

In [36]:
from statsmodels.api import OLS,add_constant
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
In [37]:
rsq=round(model.rsquared_adj,4)
rsq
Out[37]:
0.9279
In [38]:
model.pvalues.sort_values()
Out[38]:
Neighborhood_StoneBr    9.279455e-12
BsmtExposure_Gd         5.010083e-09
OverallQual             7.418255e-09
BsmtFinSF1              1.349800e-08
TotalBsmtSF             2.484970e-07
                            ...     
GarageType_2Types                NaN
GarageQual_Po                    NaN
PoolQC_Ex                        NaN
PoolQC_Fa                        NaN
MiscFeature_TenC                 NaN
Length: 286, dtype: float64
In [39]:
col_to_drop=model.pvalues.sort_values().index[-1]
In [40]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop SaleType_CWD
In [41]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop PoolQC_Fa
In [42]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop PoolQC_Ex
In [43]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop GarageQual_Po
In [44]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop GarageType_2Types
In [45]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop Functional_Sev
In [46]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop Electrical_Mix
In [47]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop HeatingQC_Po
In [48]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop KitchenQual_Fa
In [49]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop HouseStyle_1.5Fin
In [50]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop RoofStyle_Shed
In [51]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop MiscFeature_Gar2
In [52]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9332
column to drop MiscFeature_Shed
In [53]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9333
column to drop BsmtFinType1_BLQ
In [54]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9333
column to drop BsmtExposure_Av
In [55]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9333
column to drop Condition2_PosN
In [56]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9333
column to drop LandSlope_Gtl
In [57]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9333
column to drop RoofMatl_Tar&Grv
In [58]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9333
column to drop RoofStyle_Flat
In [59]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9333
column to drop Exterior1st_CemntBd
In [220]:
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)

Adjusted r_squared 0.9401
column to drop LotShape_IR2
In [221]:
Xnew.columns
Out[221]:
Index(['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',
       'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'TotalBsmtSF', '1stFlrSF',
       ...
       'GarageFinish_Unf', 'GarageQual_Fa', 'PavedDrive_N', 'PavedDrive_Y',
       'PoolQC_Gd', 'Fence_MnPrv', 'SaleCondition_Abnorml',
       'SaleCondition_AdjLand', 'SaleCondition_Alloca',
       'SaleCondition_Partial'],
      dtype='object', length=105)
In [222]:
len(Xnew.columns)
Out[222]:
105

Applying linear regression¶

Finding Mean Absolute training & testing error¶

In [223]:
from sklearn.linear_model import LinearRegression
lm=LinearRegression()
model=lm.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)
from sklearn.metrics import mean_squared_error,mean_absolute_error
In [224]:
tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)
In [225]:
print('train_err',tr_err)

train_err 245581225.50966182
In [226]:
print('test_err',ts_err)

test_err 466091937.34134614
In [227]:
print('train_ab',tr_ab)

train_ab 10878.526570048309
In [228]:
print('test_ab',ts_ab)

test_ab 14494.485576923076

Model is overfitted (testing error > training error)¶

Applying Regularisation Technique¶

By Ridge¶

In [229]:
from sklearn.linear_model import Ridge
In [230]:
rr=Ridge(alpha=0.2)
model=rr.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)

tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)
In [231]:
print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)

train_err 245918989.70185483
test_err 446292659.093027
train_ab 10879.632101245055
test_ab 14234.733770509936

Storing alpha range in a list for cross validation¶

In [232]:
w=[]
e=0.01
for i in range(0,500,1):
    w.append(e)
    e=round(e+0.01,2)
In [233]:
w
Out[233]:
[0.01,
 0.02,
 0.03,
 0.04,
 0.05,
 0.06,
 0.07,
 0.08,
 0.09,
 0.1,
 0.11,
 0.12,
 0.13,
 0.14,
 0.15,
 0.16,
 0.17,
 0.18,
 0.19,
 0.2,
 0.21,
 0.22,
 0.23,
 0.24,
 0.25,
 0.26,
 0.27,
 0.28,
 0.29,
 0.3,
 0.31,
 0.32,
 0.33,
 0.34,
 0.35,
 0.36,
 0.37,
 0.38,
 0.39,
 0.4,
 0.41,
 0.42,
 0.43,
 0.44,
 0.45,
 0.46,
 0.47,
 0.48,
 0.49,
 0.5,
 0.51,
 0.52,
 0.53,
 0.54,
 0.55,
 0.56,
 0.57,
 0.58,
 0.59,
 0.6,
 0.61,
 0.62,
 0.63,
 0.64,
 0.65,
 0.66,
 0.67,
 0.68,
 0.69,
 0.7,
 0.71,
 0.72,
 0.73,
 0.74,
 0.75,
 0.76,
 0.77,
 0.78,
 0.79,
 0.8,
 0.81,
 0.82,
 0.83,
 0.84,
 0.85,
 0.86,
 0.87,
 0.88,
 0.89,
 0.9,
 0.91,
 0.92,
 0.93,
 0.94,
 0.95,
 0.96,
 0.97,
 0.98,
 0.99,
 1.0,
 1.01,
 1.02,
 1.03,
 1.04,
 1.05,
 1.06,
 1.07,
 1.08,
 1.09,
 1.1,
 1.11,
 1.12,
 1.13,
 1.14,
 1.15,
 1.16,
 1.17,
 1.18,
 1.19,
 1.2,
 1.21,
 1.22,
 1.23,
 1.24,
 1.25,
 1.26,
 1.27,
 1.28,
 1.29,
 1.3,
 1.31,
 1.32,
 1.33,
 1.34,
 1.35,
 1.36,
 1.37,
 1.38,
 1.39,
 1.4,
 1.41,
 1.42,
 1.43,
 1.44,
 1.45,
 1.46,
 1.47,
 1.48,
 1.49,
 1.5,
 1.51,
 1.52,
 1.53,
 1.54,
 1.55,
 1.56,
 1.57,
 1.58,
 1.59,
 1.6,
 1.61,
 1.62,
 1.63,
 1.64,
 1.65,
 1.66,
 1.67,
 1.68,
 1.69,
 1.7,
 1.71,
 1.72,
 1.73,
 1.74,
 1.75,
 1.76,
 1.77,
 1.78,
 1.79,
 1.8,
 1.81,
 1.82,
 1.83,
 1.84,
 1.85,
 1.86,
 1.87,
 1.88,
 1.89,
 1.9,
 1.91,
 1.92,
 1.93,
 1.94,
 1.95,
 1.96,
 1.97,
 1.98,
 1.99,
 2.0,
 2.01,
 2.02,
 2.03,
 2.04,
 2.05,
 2.06,
 2.07,
 2.08,
 2.09,
 2.1,
 2.11,
 2.12,
 2.13,
 2.14,
 2.15,
 2.16,
 2.17,
 2.18,
 2.19,
 2.2,
 2.21,
 2.22,
 2.23,
 2.24,
 2.25,
 2.26,
 2.27,
 2.28,
 2.29,
 2.3,
 2.31,
 2.32,
 2.33,
 2.34,
 2.35,
 2.36,
 2.37,
 2.38,
 2.39,
 2.4,
 2.41,
 2.42,
 2.43,
 2.44,
 2.45,
 2.46,
 2.47,
 2.48,
 2.49,
 2.5,
 2.51,
 2.52,
 2.53,
 2.54,
 2.55,
 2.56,
 2.57,
 2.58,
 2.59,
 2.6,
 2.61,
 2.62,
 2.63,
 2.64,
 2.65,
 2.66,
 2.67,
 2.68,
 2.69,
 2.7,
 2.71,
 2.72,
 2.73,
 2.74,
 2.75,
 2.76,
 2.77,
 2.78,
 2.79,
 2.8,
 2.81,
 2.82,
 2.83,
 2.84,
 2.85,
 2.86,
 2.87,
 2.88,
 2.89,
 2.9,
 2.91,
 2.92,
 2.93,
 2.94,
 2.95,
 2.96,
 2.97,
 2.98,
 2.99,
 3.0,
 3.01,
 3.02,
 3.03,
 3.04,
 3.05,
 3.06,
 3.07,
 3.08,
 3.09,
 3.1,
 3.11,
 3.12,
 3.13,
 3.14,
 3.15,
 3.16,
 3.17,
 3.18,
 3.19,
 3.2,
 3.21,
 3.22,
 3.23,
 3.24,
 3.25,
 3.26,
 3.27,
 3.28,
 3.29,
 3.3,
 3.31,
 3.32,
 3.33,
 3.34,
 3.35,
 3.36,
 3.37,
 3.38,
 3.39,
 3.4,
 3.41,
 3.42,
 3.43,
 3.44,
 3.45,
 3.46,
 3.47,
 3.48,
 3.49,
 3.5,
 3.51,
 3.52,
 3.53,
 3.54,
 3.55,
 3.56,
 3.57,
 3.58,
 3.59,
 3.6,
 3.61,
 3.62,
 3.63,
 3.64,
 3.65,
 3.66,
 3.67,
 3.68,
 3.69,
 3.7,
 3.71,
 3.72,
 3.73,
 3.74,
 3.75,
 3.76,
 3.77,
 3.78,
 3.79,
 3.8,
 3.81,
 3.82,
 3.83,
 3.84,
 3.85,
 3.86,
 3.87,
 3.88,
 3.89,
 3.9,
 3.91,
 3.92,
 3.93,
 3.94,
 3.95,
 3.96,
 3.97,
 3.98,
 3.99,
 4.0,
 4.01,
 4.02,
 4.03,
 4.04,
 4.05,
 4.06,
 4.07,
 4.08,
 4.09,
 4.1,
 4.11,
 4.12,
 4.13,
 4.14,
 4.15,
 4.16,
 4.17,
 4.18,
 4.19,
 4.2,
 4.21,
 4.22,
 4.23,
 4.24,
 4.25,
 4.26,
 4.27,
 4.28,
 4.29,
 4.3,
 4.31,
 4.32,
 4.33,
 4.34,
 4.35,
 4.36,
 4.37,
 4.38,
 4.39,
 4.4,
 4.41,
 4.42,
 4.43,
 4.44,
 4.45,
 4.46,
 4.47,
 4.48,
 4.49,
 4.5,
 4.51,
 4.52,
 4.53,
 4.54,
 4.55,
 4.56,
 4.57,
 4.58,
 4.59,
 4.6,
 4.61,
 4.62,
 4.63,
 4.64,
 4.65,
 4.66,
 4.67,
 4.68,
 4.69,
 4.7,
 4.71,
 4.72,
 4.73,
 4.74,
 4.75,
 4.76,
 4.77,
 4.78,
 4.79,
 4.8,
 4.81,
 4.82,
 4.83,
 4.84,
 4.85,
 4.86,
 4.87,
 4.88,
 4.89,
 4.9,
 4.91,
 4.92,
 4.93,
 4.94,
 4.95,
 4.96,
 4.97,
 4.98,
 4.99,
 5.0]

Cross Validation¶

Grid Search CV¶

In [234]:
rr=Ridge()
tg={'alpha':w}

from sklearn.model_selection import GridSearchCV

cv=GridSearchCV(rr,tg,scoring='neg_mean_absolute_error',cv=4)
cvmodel=cv.fit(Xnew,Y)
In [235]:
cvmodel.best_params_
Out[235]:
{'alpha': 2.36}
In [236]:
rr=Ridge(alpha=2.36)
model=rr.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)

tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)
In [237]:
print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)

train_err 257484090.6451184
test_err 380859377.1935505
train_ab 11105.321082717273
test_ab 13265.776840227754
In [238]:
cvmodel.best_estimator_
Out[238]:
Ridge(alpha=2.36)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Ridge(alpha=2.36)

Reading Testing data set¶

In [239]:
df2=pd.read_csv('D:/testing_set.csv')
df2
Out[239]:
Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition
0 1461 20 RH 80.0 11622 Pave NaN Reg Lvl AllPub ... 120 0 NaN MnPrv NaN 0 6 2010 WD Normal
1 1462 20 RL 81.0 14267 Pave NaN IR1 Lvl AllPub ... 0 0 NaN NaN Gar2 12500 6 2010 WD Normal
2 1463 60 RL 74.0 13830 Pave NaN IR1 Lvl AllPub ... 0 0 NaN MnPrv NaN 0 3 2010 WD Normal
3 1464 60 RL 78.0 9978 Pave NaN IR1 Lvl AllPub ... 0 0 NaN NaN NaN 0 6 2010 WD Normal
4 1465 120 RL 43.0 5005 Pave NaN IR1 HLS AllPub ... 144 0 NaN NaN NaN 0 1 2010 WD Normal
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 2915 160 RM 21.0 1936 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 6 2006 WD Normal
1455 2916 160 RM 21.0 1894 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 4 2006 WD Abnorml
1456 2917 20 RL 160.0 20000 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 9 2006 WD Abnorml
1457 2918 85 RL 62.0 10441 Pave NaN Reg Lvl AllPub ... 0 0 NaN MnPrv Shed 700 7 2006 WD Normal
1458 2919 60 RL 74.0 9627 Pave NaN Reg Lvl AllPub ... 0 0 NaN NaN NaN 0 11 2006 WD Normal

1459 rows × 80 columns

In [240]:
df2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1459 entries, 0 to 1458
Data columns (total 80 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1459 non-null   int64  
 1   MSSubClass     1459 non-null   int64  
 2   MSZoning       1455 non-null   object 
 3   LotFrontage    1232 non-null   float64
 4   LotArea        1459 non-null   int64  
 5   Street         1459 non-null   object 
 6   Alley          107 non-null    object 
 7   LotShape       1459 non-null   object 
 8   LandContour    1459 non-null   object 
 9   Utilities      1457 non-null   object 
 10  LotConfig      1459 non-null   object 
 11  LandSlope      1459 non-null   object 
 12  Neighborhood   1459 non-null   object 
 13  Condition1     1459 non-null   object 
 14  Condition2     1459 non-null   object 
 15  BldgType       1459 non-null   object 
 16  HouseStyle     1459 non-null   object 
 17  OverallQual    1459 non-null   int64  
 18  OverallCond    1459 non-null   int64  
 19  YearBuilt      1459 non-null   int64  
 20  YearRemodAdd   1459 non-null   int64  
 21  RoofStyle      1459 non-null   object 
 22  RoofMatl       1459 non-null   object 
 23  Exterior1st    1458 non-null   object 
 24  Exterior2nd    1458 non-null   object 
 25  MasVnrType     1443 non-null   object 
 26  MasVnrArea     1444 non-null   float64
 27  ExterQual      1459 non-null   object 
 28  ExterCond      1459 non-null   object 
 29  Foundation     1459 non-null   object 
 30  BsmtQual       1415 non-null   object 
 31  BsmtCond       1414 non-null   object 
 32  BsmtExposure   1415 non-null   object 
 33  BsmtFinType1   1417 non-null   object 
 34  BsmtFinSF1     1458 non-null   float64
 35  BsmtFinType2   1417 non-null   object 
 36  BsmtFinSF2     1458 non-null   float64
 37  BsmtUnfSF      1458 non-null   float64
 38  TotalBsmtSF    1458 non-null   float64
 39  Heating        1459 non-null   object 
 40  HeatingQC      1459 non-null   object 
 41  CentralAir     1459 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1459 non-null   int64  
 44  2ndFlrSF       1459 non-null   int64  
 45  LowQualFinSF   1459 non-null   int64  
 46  GrLivArea      1459 non-null   int64  
 47  BsmtFullBath   1457 non-null   float64
 48  BsmtHalfBath   1457 non-null   float64
 49  FullBath       1459 non-null   int64  
 50  HalfBath       1459 non-null   int64  
 51  BedroomAbvGr   1459 non-null   int64  
 52  KitchenAbvGr   1459 non-null   int64  
 53  KitchenQual    1458 non-null   object 
 54  TotRmsAbvGrd   1459 non-null   int64  
 55  Functional     1457 non-null   object 
 56  Fireplaces     1459 non-null   int64  
 57  FireplaceQu    729 non-null    object 
 58  GarageType     1383 non-null   object 
 59  GarageYrBlt    1381 non-null   float64
 60  GarageFinish   1381 non-null   object 
 61  GarageCars     1458 non-null   float64
 62  GarageArea     1458 non-null   float64
 63  GarageQual     1381 non-null   object 
 64  GarageCond     1381 non-null   object 
 65  PavedDrive     1459 non-null   object 
 66  WoodDeckSF     1459 non-null   int64  
 67  OpenPorchSF    1459 non-null   int64  
 68  EnclosedPorch  1459 non-null   int64  
 69  3SsnPorch      1459 non-null   int64  
 70  ScreenPorch    1459 non-null   int64  
 71  PoolArea       1459 non-null   int64  
 72  PoolQC         3 non-null      object 
 73  Fence          290 non-null    object 
 74  MiscFeature    51 non-null     object 
 75  MiscVal        1459 non-null   int64  
 76  MoSold         1459 non-null   int64  
 77  YrSold         1459 non-null   int64  
 78  SaleType       1458 non-null   object 
 79  SaleCondition  1459 non-null   object 
dtypes: float64(11), int64(26), object(43)
memory usage: 912.0+ KB
In [241]:
df2.shape
Out[241]:
(1459, 80)

Missing Data Treatment in testing dataset (Filling Null values)¶

In [242]:
df2.isna().sum()
Out[242]:
Id                 0
MSSubClass         0
MSZoning           4
LotFrontage      227
LotArea            0
                ... 
MiscVal            0
MoSold             0
YrSold             0
SaleType           1
SaleCondition      0
Length: 80, dtype: int64
In [243]:
for i in df2.columns:
        if (df2[i].isna().sum())>0:
            if df2[i].dtypes=='object':
                x=df2[i].mode()[0]
                df2[i]=df2[i].fillna(x)
            else:
                x=df2[i].mean()
                df2[i]=df2[i].fillna(x)
In [244]:
df2.isna().sum()
Out[244]:
Id               0
MSSubClass       0
MSZoning         0
LotFrontage      0
LotArea          0
                ..
MiscVal          0
MoSold           0
YrSold           0
SaleType         0
SaleCondition    0
Length: 80, dtype: int64

Droping irrelevant columns from testing dataset¶

Seperating Input & Output features from testing dataset (Xts & Yts)¶

In [246]:
Xts=df2.drop(labels=['Id','LowQualFinSF','MiscVal'],axis=1)
In [247]:
Xts.shape
Out[247]:
(1459, 77)

Seperating Categorical & Continuous columns in testing dataset¶

In [249]:
cat_ts=[]
con_ts=[]
for i in Xts.columns:
    if Xts[i].dtypes=='object':
        cat_ts.append(i)
    else:
        con_ts.append(i)
print(cat_ts)
print(con_ts)

['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']
['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MoSold', 'YrSold']
In [250]:
cat_ts=['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 
        'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 
        'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 
        'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 
        'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 
        'SaleCondition']
con_ts=['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 
        'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 
        'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 
        'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 
        'MoSold', 'YrSold']

Standardisation of Continuous¶

In [251]:
from sklearn.preprocessing import StandardScaler
ss=StandardScaler()
X1ts=pd.DataFrame(ss.fit_transform(Xts[con_ts]),columns=con_ts)
X1ts
Out[251]:
MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MoSold YrSold
0 -0.874711 0.555587 0.363929 -0.751101 0.400766 -0.340945 -1.072885 -0.570108 0.063295 0.517348 ... -0.988013 1.185945 0.366678 -0.701628 -0.360738 -0.088827 1.818960 -0.057227 -0.038281 1.713905
1 -0.874711 0.604239 0.897861 -0.054877 0.400766 -0.439695 -1.214908 0.041273 1.063392 -0.297903 ... -0.988013 -0.741213 2.347867 -0.178826 -0.360738 -0.088827 -0.301543 -0.057227 -0.038281 1.713905
2 0.061351 0.263676 0.809646 -0.751101 -0.497418 0.844059 0.678742 -0.570108 0.773254 -0.297903 ... 0.301623 0.042559 0.930495 -0.207871 -0.360738 -0.088827 -0.301543 -0.057227 -1.140614 1.713905
3 0.061351 0.458284 0.032064 -0.054877 0.400766 0.876976 0.678742 -0.456889 0.357829 -0.297903 ... 0.301623 -0.012766 2.089451 -0.178826 -0.360738 -0.088827 -0.301543 -0.057227 -0.038281 1.713905
4 1.465443 -1.244533 -0.971808 1.337571 -0.497418 0.679475 0.394694 -0.570108 -0.387298 -0.297903 ... 0.301623 0.153210 -0.729632 0.489198 -0.360738 -0.088827 2.243060 -0.057227 -1.875504 1.713905
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 2.401505 -2.314875 -1.591330 -1.447325 1.298950 -0.044694 -0.646813 -0.570108 -0.965376 -0.297903 ... -2.277648 -2.179665 -0.729632 -0.701628 -0.360738 -0.088827 -0.301543 -0.057227 -0.038281 -1.359958
1455 2.401505 -2.314875 -1.599808 -1.447325 -0.497418 -0.044694 -0.646813 -0.570108 -0.411477 -0.297903 ... -0.988013 -0.861084 -0.729632 -0.353093 -0.360738 -0.088827 -0.301543 -0.057227 -0.773170 -1.359958
1456 -0.874711 4.447740 2.055150 -0.751101 1.298950 -0.373861 0.584059 -0.570108 1.724994 -0.297903 ... 0.301623 0.475939 2.982161 -0.701628 -0.360738 -0.088827 -0.301543 -0.057227 1.064053 -1.359958
1457 0.646389 -0.320147 0.125527 -0.751101 -0.497418 0.679475 0.394694 -0.570108 -0.224645 -0.297903 ... -2.277648 -2.179665 -0.103169 -0.236915 -0.360738 -0.088827 -0.301543 -0.057227 0.329164 -1.359958
1458 0.061351 0.263676 -0.038790 0.641347 -0.497418 0.712392 0.489377 -0.037980 0.700719 -0.297903 ... 1.591258 0.817111 0.758218 -0.004559 -0.360738 -0.088827 -0.301543 -0.057227 1.798942 -1.359958

1459 rows × 34 columns

Encoding ( converting Categorical cloumns in continous)¶

OneHot Encoding¶

In [252]:
X2ts=pd.get_dummies(Xts[cat_ts])
X2ts
Out[252]:
MSZoning_C (all) MSZoning_FV MSZoning_RH MSZoning_RL MSZoning_RM Street_Grvl Street_Pave Alley_Grvl Alley_Pave LotShape_IR1 ... SaleType_ConLw SaleType_New SaleType_Oth SaleType_WD SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Family SaleCondition_Normal SaleCondition_Partial
0 0 0 1 0 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1 0 0 0 1 0 0 1 1 0 1 ... 0 0 0 1 0 0 0 0 1 0
2 0 0 0 1 0 0 1 1 0 1 ... 0 0 0 1 0 0 0 0 1 0
3 0 0 0 1 0 0 1 1 0 1 ... 0 0 0 1 0 0 0 0 1 0
4 0 0 0 1 0 0 1 1 0 1 ... 0 0 0 1 0 0 0 0 1 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 0 0 0 0 1 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1455 0 0 0 0 1 0 1 1 0 0 ... 0 0 0 1 1 0 0 0 0 0
1456 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 1 0 0 0 0 0
1457 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0
1458 0 0 0 1 0 0 1 1 0 0 ... 0 0 0 1 0 0 0 0 1 0

1459 rows × 234 columns

Joining X1ts[Cat] & X2ts[Con]¶

In [253]:
Xnew_ts=X1ts.join(X2ts)
In [254]:
Xnew_ts.shape
Out[254]:
(1459, 268)

Reindexing Xnew_ts¶

In [255]:
Xnew_ts.index=range(0,1459)
In [256]:
keep=[]
drop=[]
for i in Xnew_ts.columns:
    if i in Xnew.columns:
        keep.append(i)
    else:
        drop.append(i)
In [257]:
print(keep)

['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'Fireplaces', 'GarageArea', 'WoodDeckSF', '3SsnPorch', 'PoolArea', 'MSZoning_C (all)', 'MSZoning_FV', 'MSZoning_RM', 'Alley_Grvl', 'Alley_Pave', 'LotShape_IR2', 'LandContour_Bnk', 'LandContour_HLS', 'LandContour_Low', 'LandContour_Lvl', 'Utilities_AllPub', 'LotConfig_Corner', 'LotConfig_CulDSac', 'LotConfig_Inside', 'Neighborhood_Blmngtn', 'Neighborhood_BrDale', 'Neighborhood_Crawfor', 'Neighborhood_Edwards', 'Neighborhood_NPkVill', 'Neighborhood_NWAmes', 'Neighborhood_NoRidge', 'Neighborhood_NridgHt', 'Neighborhood_OldTown', 'Neighborhood_StoneBr', 'Condition1_Artery', 'Condition1_PosA', 'Condition1_RRAe', 'BldgType_Duplex', 'BldgType_TwnhsE', 'HouseStyle_1.5Unf', 'HouseStyle_SFoyer', 'HouseStyle_SLvl', 'RoofMatl_CompShg', 'Exterior1st_HdBoard', 'Exterior1st_Plywood', 'Exterior1st_Stucco', 'Exterior1st_VinylSd', 'Exterior1st_Wd Sdng', 'Exterior1st_WdShing', 'Exterior2nd_ImStucc', 'Exterior2nd_Stucco', 'Exterior2nd_VinylSd', 'Exterior2nd_Wd Sdng', 'MasVnrType_Stone', 'ExterQual_Ex', 'ExterCond_TA', 'Foundation_BrkTil', 'Foundation_Slab', 'Foundation_Stone', 'BsmtQual_Ex', 'BsmtQual_Fa', 'BsmtCond_Gd', 'BsmtCond_TA', 'BsmtExposure_Gd', 'BsmtExposure_Mn', 'BsmtExposure_No', 'BsmtFinType1_GLQ', 'BsmtFinType1_LwQ', 'BsmtFinType2_GLQ', 'Heating_Wall', 'HeatingQC_Ex', 'HeatingQC_Fa', 'HeatingQC_Gd', 'HeatingQC_TA', 'CentralAir_N', 'CentralAir_Y', 'Electrical_FuseA', 'KitchenQual_Ex', 'Functional_Mod', 'Functional_Typ', 'FireplaceQu_Po', 'GarageType_Attchd', 'GarageFinish_Fin', 'GarageFinish_RFn', 'GarageFinish_Unf', 'GarageQual_Fa', 'PavedDrive_N', 'PavedDrive_Y', 'PoolQC_Gd', 'Fence_MnPrv', 'SaleCondition_Abnorml', 'SaleCondition_AdjLand', 'SaleCondition_Alloca', 'SaleCondition_Partial']
In [258]:
print(drop)

['LotFrontage', 'BsmtFinSF2', 'BsmtUnfSF', 'GrLivArea', 'BsmtFullBath', 'TotRmsAbvGrd', 'GarageYrBlt', 'GarageCars', 'OpenPorchSF', 'EnclosedPorch', 'ScreenPorch', 'MoSold', 'YrSold', 'MSZoning_RH', 'MSZoning_RL', 'Street_Grvl', 'Street_Pave', 'LotShape_IR1', 'LotShape_IR3', 'LotShape_Reg', 'LotConfig_FR2', 'LotConfig_FR3', 'LandSlope_Gtl', 'LandSlope_Mod', 'LandSlope_Sev', 'Neighborhood_Blueste', 'Neighborhood_BrkSide', 'Neighborhood_ClearCr', 'Neighborhood_CollgCr', 'Neighborhood_Gilbert', 'Neighborhood_IDOTRR', 'Neighborhood_MeadowV', 'Neighborhood_Mitchel', 'Neighborhood_NAmes', 'Neighborhood_SWISU', 'Neighborhood_Sawyer', 'Neighborhood_SawyerW', 'Neighborhood_Somerst', 'Neighborhood_Timber', 'Neighborhood_Veenker', 'Condition1_Feedr', 'Condition1_Norm', 'Condition1_PosN', 'Condition1_RRAn', 'Condition1_RRNe', 'Condition1_RRNn', 'Condition2_Artery', 'Condition2_Feedr', 'Condition2_Norm', 'Condition2_PosA', 'Condition2_PosN', 'BldgType_1Fam', 'BldgType_2fmCon', 'BldgType_Twnhs', 'HouseStyle_1.5Fin', 'HouseStyle_1Story', 'HouseStyle_2.5Unf', 'HouseStyle_2Story', 'RoofStyle_Flat', 'RoofStyle_Gable', 'RoofStyle_Gambrel', 'RoofStyle_Hip', 'RoofStyle_Mansard', 'RoofStyle_Shed', 'RoofMatl_Tar&Grv', 'RoofMatl_WdShake', 'RoofMatl_WdShngl', 'Exterior1st_AsbShng', 'Exterior1st_AsphShn', 'Exterior1st_BrkComm', 'Exterior1st_BrkFace', 'Exterior1st_CBlock', 'Exterior1st_CemntBd', 'Exterior1st_MetalSd', 'Exterior2nd_AsbShng', 'Exterior2nd_AsphShn', 'Exterior2nd_Brk Cmn', 'Exterior2nd_BrkFace', 'Exterior2nd_CBlock', 'Exterior2nd_CmentBd', 'Exterior2nd_HdBoard', 'Exterior2nd_MetalSd', 'Exterior2nd_Plywood', 'Exterior2nd_Stone', 'Exterior2nd_Wd Shng', 'MasVnrType_BrkCmn', 'MasVnrType_BrkFace', 'MasVnrType_None', 'ExterQual_Fa', 'ExterQual_Gd', 'ExterQual_TA', 'ExterCond_Ex', 'ExterCond_Fa', 'ExterCond_Gd', 'ExterCond_Po', 'Foundation_CBlock', 'Foundation_PConc', 'Foundation_Wood', 'BsmtQual_Gd', 'BsmtQual_TA', 'BsmtCond_Fa', 'BsmtCond_Po', 'BsmtExposure_Av', 'BsmtFinType1_ALQ', 'BsmtFinType1_BLQ', 'BsmtFinType1_Rec', 'BsmtFinType1_Unf', 'BsmtFinType2_ALQ', 'BsmtFinType2_BLQ', 'BsmtFinType2_LwQ', 'BsmtFinType2_Rec', 'BsmtFinType2_Unf', 'Heating_GasA', 'Heating_GasW', 'Heating_Grav', 'HeatingQC_Po', 'Electrical_FuseF', 'Electrical_FuseP', 'Electrical_SBrkr', 'KitchenQual_Fa', 'KitchenQual_Gd', 'KitchenQual_TA', 'Functional_Maj1', 'Functional_Maj2', 'Functional_Min1', 'Functional_Min2', 'Functional_Sev', 'FireplaceQu_Ex', 'FireplaceQu_Fa', 'FireplaceQu_Gd', 'FireplaceQu_TA', 'GarageType_2Types', 'GarageType_Basment', 'GarageType_BuiltIn', 'GarageType_CarPort', 'GarageType_Detchd', 'GarageQual_Gd', 'GarageQual_Po', 'GarageQual_TA', 'GarageCond_Ex', 'GarageCond_Fa', 'GarageCond_Gd', 'GarageCond_Po', 'GarageCond_TA', 'PavedDrive_P', 'PoolQC_Ex', 'Fence_GdPrv', 'Fence_GdWo', 'Fence_MnWw', 'MiscFeature_Gar2', 'MiscFeature_Othr', 'MiscFeature_Shed', 'SaleType_COD', 'SaleType_CWD', 'SaleType_Con', 'SaleType_ConLD', 'SaleType_ConLI', 'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD', 'SaleCondition_Family', 'SaleCondition_Normal']
In [259]:
len(Xnew_ts.columns)
Out[259]:
268
In [260]:
Xnewtest=Xnew_ts[keep]
In [261]:
len(Xnewtest.columns)
Out[261]:
105
In [262]:
Xnewtest
Out[262]:
MSSubClass LotArea OverallQual OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 TotalBsmtSF 1stFlrSF ... GarageFinish_Unf GarageQual_Fa PavedDrive_N PavedDrive_Y PoolQC_Gd Fence_MnPrv SaleCondition_Abnorml SaleCondition_AdjLand SaleCondition_Alloca SaleCondition_Partial
0 -0.874711 0.363929 -0.751101 0.400766 -0.340945 -1.072885 -0.570108 0.063295 -0.370808 -0.654561 ... 1 0 0 1 0 1 0 0 0 0
1 -0.874711 0.897861 -0.054877 0.400766 -0.439695 -1.214908 0.041273 1.063392 0.639144 0.433298 ... 1 0 0 1 0 1 0 0 0 0
2 0.061351 0.809646 -0.751101 -0.497418 0.844059 0.678742 -0.570108 0.773254 -0.266876 -0.574165 ... 0 0 0 1 0 1 0 0 0 0
3 0.061351 0.032064 -0.054877 0.400766 0.876976 0.678742 -0.456889 0.357829 -0.271395 -0.579190 ... 0 0 0 1 0 1 0 0 0 0
4 1.465443 -0.971808 1.337571 -0.497418 0.679475 0.394694 -0.570108 -0.387298 0.528434 0.310192 ... 0 0 0 1 0 1 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1454 2.401505 -1.591330 -1.447325 1.298950 -0.044694 -0.646813 -0.570108 -0.965376 -1.129968 -1.533893 ... 1 0 0 1 0 1 0 0 0 0
1455 2.401505 -1.599808 -1.447325 -0.497418 -0.044694 -0.646813 -0.570108 -0.411477 -1.129968 -1.533893 ... 1 0 0 1 0 1 1 0 0 0
1456 -0.874711 2.055150 -0.751101 1.298950 -0.373861 0.584059 -0.570108 1.724994 0.401907 0.169499 ... 1 0 0 1 0 1 1 0 0 0
1457 0.646389 0.125527 -0.751101 -0.497418 0.679475 0.394694 -0.570108 -0.224645 -0.303026 -0.468645 ... 1 0 0 1 0 1 0 0 0 0
1458 0.061351 -0.038790 0.641347 -0.497418 0.712392 0.489377 -0.037980 0.700719 -0.113237 -0.403324 ... 0 0 0 1 0 1 0 0 0 0

1459 rows × 105 columns

In [263]:
pred=cvmodel.predict(Xnewtest)
In [264]:
pred
Out[264]:
array([138429.47870899, 181922.3828758 , 202868.94014064, ...,
       196078.94599536, 119756.13433631, 222904.36483644])
In [265]:
predn={'Output':list(pred)}
In [266]:
print(predn)

{'Output': [138429.47870898744, 181922.38287579792, 202868.94014064278, 208819.3966818965, 196351.07400424732, 175876.87481910235, 171840.8437886355, 162109.3324577118, 217398.7646522493, 116522.3678507164, 184373.89166300194, 93185.85826060869, 88185.70322395136, 139578.4249525212, 95311.86454471792, 357983.38620497286, 254734.11956668185, 301680.01665617776, 306996.4268545519, 442605.7640678659, 310106.8426112622, 207064.08962930372, 183312.26713828515, 158973.36515351606, 186585.4961614316, 197269.742467857, 316747.7085790145, 219800.42414222116, 203807.8391175387, 249273.44036505, 198921.34156240843, 99194.6331966687, 203348.54848009616, 263987.5642483526, 280159.96298725903, 244068.69101453363, 177820.6950376915, 166068.76574102143, 156801.67012471927, 148089.50223901714, 181325.88028172636, 142317.15051009858, 226360.7774484935, 244758.88685853942, 231131.56173733255, 199156.61867425474, 268538.13356157125, 222324.81312315533, 162138.21168641376, 147865.7118012545, 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In [267]:
len(predn)
Out[267]:
1
In [268]:
ID=df2['Id']
In [269]:
ID
Out[269]:
0       1461
1       1462
2       1463
3       1464
4       1465
        ... 
1454    2915
1455    2916
1456    2917
1457    2918
1458    2919
Name: Id, Length: 1459, dtype: int64
In [270]:
df_new=pd.DataFrame(ID)
In [271]:
df_new
Out[271]:
Id
0 1461
1 1462
2 1463
3 1464
4 1465
... ...
1454 2915
1455 2916
1456 2917
1457 2918
1458 2919

1459 rows × 1 columns

In [272]:
df_news=pd.DataFrame(predn)
In [273]:
df_news
Out[273]:
Output
0 138429.478709
1 181922.382876
2 202868.940141
3 208819.396682
4 196351.074004
... ...
1454 79205.201930
1455 81236.386079
1456 196078.945995
1457 119756.134336
1458 222904.364836

1459 rows × 1 columns

Final output Dataset¶

In [274]:
df_final=df_new.join(df_news)
In [275]:
df_final
Out[275]:
Id Output
0 1461 138429.478709
1 1462 181922.382876
2 1463 202868.940141
3 1464 208819.396682
4 1465 196351.074004
... ... ...
1454 2915 79205.201930
1455 2916 81236.386079
1456 2917 196078.945995
1457 2918 119756.134336
1458 2919 222904.364836

1459 rows × 2 columns

In [ ]: